Mandar Kulkarni

Computational chemist

[Mandar Kulkarni @mandar5335]

@chartkingzzz

[Science News @ScienceNews]

Designing better proteins usually takes round after round of trial and error. A new machine learning framework can do it in a single pass — predicting which combinations of mutations will work well together. It could speed up drug design and gene editing.
https://t.co/Sx7VjyQvoW

[Science Magazine @ScienceMagazine]

Despite progress in drug discovery, approximately 90% of druggable disease targets still lack small-molecule therapies.

Although virtual screening can accelerate hit identification, traditional methods such as molecular docking remain too slow for genome-scale applications.

In a new Science study, researchers introduce DrugCLIP, a contrastive learning framework that virtually screens small molecules and protein pockets, analyzing protein-ligand interactions 10 million times faster than most standard molecular docking approaches. https://t.co/zr0CdXAaug

[Mandar Kulkarni @mandar5335]

@chartkingzzz Thanks!

[Mandar Kulkarni @mandar5335]

@chartkingzzz Thanks 🙏🏼

[Mandar Kulkarni @mandar5335]

@chartkingzzz Is there any possibility of a significant downward trend based on the ABCD pattern if 61.8% level breaks on the Nifty 50 Week chart?

[Alessandra Magistrato @Magistrato_Lab]

We're still accepting applications for poster presentations at the CECAM conference "RNA We're still We are sill accepting applications for poster presentation at 'RNA Modelling Across Scales" conference. Apply now: https://t.co/vohRDdDOVo Organized with @BussiGio @devivo_marco

[Das Lab @RDasLab]

Today we launched the Stanford RNA 3D Folding challenge! @kaggle @StanfordMed @HHMINews https://t.co/5w9gInSARu (1/N)

[Mandar Kulkarni @mandar5335]

@ArnabPune Happy birthday, Sir 🎂🎂

[pen(Taka) @iwatobipen]

Open source structure preparation package for small molecule virtual screening #RDKit #OSS #Cheminformatics https://t.co/zVdQ5CwmtS

[Das Lab @RDasLab]

Calling the #moleculardynamics community – first CASP target ever for predicting water (ensembles) around a macromolecule: https://t.co/HrYSuBFTMq Will be evaluated by direct comparison to #cryoEM on the Tetrahymena ribozyme #RNA!

[Olexandr Isayev 🇺🇦🇺🇸 @olexandr]

Synthetic chemists looking at AI de novo generated molecules 🤖#compchem

[non aesthetic things @PicturesFoIder]

How religion spread around the world

[Min Choi @minchoi]

AI will disrupt Hollywood (Part 41)

From music videos, short films, commercials, and claymation, AI creativity never stops.

10 examples:

[Min Choi @minchoi]

It's only been just over a week since OpenAI dropped GPT-4o.

And people can't stop getting creative with it.

10 wild examples:

[Das Lab @RDasLab]

#CASP16 has begun! First #RNA target R1203 is available for 3D structure prediction. It's an important regulatory element in HIV. Will #deeplearning outperform humans? https://t.co/NpoqWqtY7U

[Rommie Amaro @RommieAmaro]

🚨 APOBEC3B (A3B) is a major source of🧬mutation in cancer

The active site is like Teflon (not much binds) BUT @Ozlem__Dmr discovered a druggable allosteric site 👀

Experiments by @DanHarki @_ChemKat @HarrisLabUTHSA 💪

https://t.co/sAdFy42Mw0

[Zoe Cournia @zoecournia]

New competition to develop machine learning (ML) models to predict the binding affinity of small molecules to specific protein targets using the Big Encoded Library for Chemical Assessment (BELKA). With prizes! Deadline 9 July 2024. #drugdesign
https://t.co/nV29LaI074